Space-time mapping of wasting among children under the age of five years in Somalia from 2007 to 2010

Damaris Kinyoki, James Berkley, Grainne Moloney, Elijah Odundo, Ngianga-Bakwin Kandala, Abdisalan Noor

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21 Citations (Scopus)
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Abstract

Objective - To determine the sub-national seasonal prevalence and trends in wasting from 2007 to 2010 among children under the age of five years in Somalia using remote sensing and household survey data from nutritional surveys by location. Methods - Bayesian hierarchical space-time model in integrated nested Laplace approximations (INLA) was implemented using a stochastic partial differential equation (SPDE) approach to produce risk maps of wasting at 1×1 km spatial resolution and predict to seasons in each year of study from 2007 to 2010. Results - The prevalence of wasting is generally at critical levels throughout the country, with most of the areas remaining in the upper classes of critical and very critical levels. There is minimal variation in wasting from year-to-year, but a well-defined seasonal variation is observed. The mean difference of the prevalence of wasting between the dry and wet season ranges from 0% - 5%. The risks of wasting in the South-central zone were highest in the Gedo (37%) and Bay (32%) regions. In Puntland the risk was highest in Nugaal (25%) and in Somaliland the risk was high in Awdal and Woqooyi Galbeed regions with 23%. Conclusion - There is a clear seasonal variation in wasting with minimal year-to-year variability from 2007 to 2010 in Somalia. The prevalence is high during the long dry season, which affects the prevalence in the preceding long rainy season. Understanding the seasonal fluctuations of wasting in different locations and at different times is important to inform timely interventions.
Original languageEnglish
Pages (from-to)77-87
JournalSpatial and Spatio-temporal Epidemiology
Volume16
Early online date9 Jan 2016
DOIs
Publication statusPublished - Feb 2016

Keywords

  • Malnutrition
  • Wasting
  • Bayesian
  • Space–time model
  • Seasonality

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